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nbs/240508-inference-naip.ipynb

@@ -8,6 +8,7 @@
    "outputs": [],
    "source": [
     "import sys\n",
+    "\n",
     "sys.path.append(\"..\")"
    ]
   },
@@ -18,21 +19,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from einops import rearrange\n",
-    "import torch\n",
-    "import stackstac\n",
-    "from pystac_client import Client\n",
-    "import boto3\n",
-    "import xarray as xr\n",
-    "import numpy as np\n",
-    "import os\n",
     "import rioxarray\n",
-    "from box import Box\n",
+    "import torch\n",
     "import yaml\n",
+    "from box import Box\n",
+    "from pystac_client import Client\n",
     "\n",
-    "from src.datamodule import ClayDataModule\n",
     "from src.model_clay_v1 import ClayMAEModule"
    ]
   },
@@ -44,11 +40,10 @@
    "outputs": [],
    "source": [
     "def plot_rgb(stack):\n",
-    "    stack.sel(band=[1, 2, 3]).plot.imshow(\n",
-    "        rgb=\"band\", vmin=0, vmax=2000, col_wrap=6\n",
-    "    )\n",
+    "    stack.sel(band=[1, 2, 3]).plot.imshow(rgb=\"band\", vmin=0, vmax=2000, col_wrap=6)\n",
     "    plt.show()\n",
-    "    \n",
+    "\n",
+    "\n",
     "def normalize_latlon(lat, lon):\n",
     "    lat = lat * np.pi / 180\n",
     "    lon = lon * np.pi / 180\n",
@@ -124,7 +119,7 @@
     "\n",
     "    # The first embedding is the class token, which is the\n",
     "    # overall single embedding. We extract that for PCA below.\n",
-    "    return unmsk_patch[:, 0, :].cpu().numpy()\n"
+    "    return unmsk_patch[:, 0, :].cpu().numpy()"
    ]
   },
   {
@@ -61598,11 +61593,10 @@
     "    assets = item.assets\n",
     "    dataset = rioxarray.open_rasterio(item.assets[\"image\"].href).sel(band=[1, 2, 3, 4])\n",
     "    print(\"dataset: \", dataset)\n",
-    "    granule_name = item.assets[\"image\"].href.split('/')[-1]\n",
+    "    granule_name = item.assets[\"image\"].href.split(\"/\")[-1]\n",
     "    stackstac_datasets.append(dataset)\n",
     "    granule_names.append(granule_name)\n",
-    "    \n",
-    "    \n",
+    "\n",
     "\n",
     "# Function to tile dataset into 256x256 image chips and drop any excess border regions\n",
     "def tile_dataset(dataset, granule_name):\n",
@@ -61628,26 +61622,27 @@
     "            y_end = y_start + 256\n",
     "\n",
     "            # Extract the tile from the cropped dataset\n",
-    "            tile = cropped_dataset.isel(x=slice(x_start, x_end), y=slice(y_start, y_end))\n",
+    "            tile = cropped_dataset.isel(\n",
+    "                x=slice(x_start, x_end), y=slice(y_start, y_end)\n",
+    "            )\n",
     "            print(tile.shape)\n",
     "\n",
     "            # Save the tile as a GeoTIFF\n",
     "            tile_path = f\"{save_dir}/{granule_name[:-4]}_{x_idx}_{y_idx}.tif\"\n",
-    "            #tile.rio.to_raster(tile_path)\n",
+    "            # tile.rio.to_raster(tile_path)\n",
     "            print(tile)\n",
     "            tiles.append(tile)\n",
-    "            \n",
+    "\n",
     "    return tiles\n",
-    "    \n",
     "\n",
     "\n",
     "# Tile each dataset\n",
     "for dataset, granule_name in zip(stackstac_datasets[0:2], granule_names[0:2]):\n",
     "    tiles = tile_dataset(dataset, granule_name)\n",
-    "    \n",
+    "\n",
     "tile_0 = tiles[0]\n",
     "\n",
-    "plot_rgb(tile_0)\n"
+    "plot_rgb(tile_0)"
    ]
   },
   {